The field of wireless communication and navigation is witnessing significant developments, driven by the need for more accurate and reliable systems. Researchers are exploring innovative approaches to improve the performance of GPS and GNSS systems, including the use of machine learning and deep learning techniques for spoofing detection and localization. Additionally, there is a growing interest in the development of new modulation techniques, such as OTFS, which offer improved resilience to Doppler effects and channel aging. The use of hybrid architectures, combining different technologies and techniques, is also becoming increasingly popular. Noteworthy papers in this area include: The paper on C/N0 Analysis-Based GPS Spoofing Detection with Variable Antenna Orientations, which proposes a novel spoofing detection strategy based on analyzing satellite Carrier-to-Noise Density Ratio variation. The paper on Hybrid CNN-Transformer Based Sparse Channel Prediction for High-Mobility OTFS Systems, which presents a novel channel prediction framework for OTFS systems using a hybrid convolutional neural network and transformer architecture.
Advancements in Wireless Communication and Navigation
Sources
C/N0 Analysis-Based GPS Spoofing Detection with Variable Antenna Orientations
Hybrid CNN-Transformer Based Sparse Channel Prediction for High-Mobility OTFS Systems
AoA Services in 5G Networks: A Framework for Real-World Implementation and Systematic Testing
Delay-Doppler Pulse Shaping in Zak-OTFS Using Hermite Basis Functions
Deep Sequence-to-Sequence Models for GNSS Spoofing Detection
Machine Learning-Based Localization Accuracy of RFID Sensor Networks via RSSI Decision Trees and CAD Modeling for Defense Applications
A Location-Aware Hybrid Deep Learning Framework for Dynamic Near-Far Field Channel Estimation in Low-Altitude UAV Communications
Bayesian Jammer Localization with a Hybrid CNN and Path-Loss Mixture of Experts
MIMO-Zak-OTFS with Superimposed Spread Pilots